[Comp-neuro] Neural Networks, Special Issue on Neuromorphic Engineering

Neural Networks - 2013 Special Issue
Neuromorphic Engineering: from Neural Systems to Brain-Like Engineered Systems

Co-Editors
Andreas Andreou, John Hopkins University, USA
Elisabetta Chicca, Bielefeld University, Germany
David Lester, University of Manchester, UK
Francesco Carlo Morabito *, University Mediterranea Reggio Calabria, Italy

Submission
Deadline for submission: May 31, 2012
Notification of acceptance: July 31, 2012
Publication: Early 2013
Format: as for normal papers in the journal (no longer than 10,000 words)
Prospective authors should visit http://ees.elsevier.com/neunet/ for information on paper submission

* Corresponding Editor
Address for early submission of proposals:
Professor Francesco Carlo Morabito
University Mediterranea
DIMET Department
E-mail address: morabito@unirc.it


The styles of computation used by biological systems are fundamentally different from those used by conventional computers: biological neural networks process information using energy-efficient asynchronous, event-driven, methods. They are adaptive, fault-tolerant, self-repairing, learn from their interactions with the environment, and can flexibly produce complex behaviours by combining multiple instances of simpler elements. These biological abilities yield a potentially attractive alternative to conventional computing strategies. A special focus of this issue is Neuromorphic VLSI systems that are composed of Very Large Scale Integrated (VLSI) devices with hybrid analog/digital circuits that implement hardware models of biological systems. When implemented in VLSI (including FPGA) technology, neuromorphic systems often have similar strategies for maximizing compactness, optimizing robustness to noise, minimizing power consumption, and increasing fault tolerance.
By emulating the neural style of computation, neuromorphic VLSI architectures can exploit to the fullest potential the features of advanced scaled VLSI processes and future emerging technologies, naturally coping with the problems that characterize them, such as device inhomogeneities, and mismatch.

In this Special Issue we call for a broad range of papers on Neuromorphic Engineering. The various contributions will describe recent developments and progress in understanding the interplay between biology and technology for the developments of bio-inspired systems that reproduce functionality and rapid processing of their biological counterpart.
This Special Issue seeks to explore the possible synergies and interactions of different perspectives.
Suggested topics of interest include, but are not limited to, the following research and application areas:
. Neuromorphic spike-based neural processing systems
. Neuromorphic event-driven sensory systems
. Neuromorphic autonomous systems for robotic applications
. Neuromorphic real-time behaving systems
. Circuits and systems for large scale neural networks
. Neuromorphic auditory processing systems
. Plasticity and learning in neuromorphic systems
. Memristors-based Neural Circuits
. System-level brain-like processing



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JProf. Dr. Elisabetta Chicca
Cognitive Interaction Technology – Center of Excellence
Bielefeld University
Universitätsstraße 21-23
33615 Bielefeld
Tel.: +49 521 106-12043
Fax : +49 521 106-12348